Irina Topalova - Academia.edu (original) (raw)

Papers by Irina Topalova

Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium .., Dec 31, 2022

Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium .., 2019

arXiv (Cornell University), Aug 16, 2012

Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium .., 2016

International Conference on Artificial Intelligence, Feb 21, 2009

International Journal of Advanced Robotic Systems, Mar 1, 2005

This paper presents an adaptive approach, based on image processing and use of self-organizing ma... more This paper presents an adaptive approach, based on image processing and use of self-organizing maps for filtering, analyzing, and determining the sigma phase percentage in metallographic images of austenitic stainless steel. In order to predict the remaining life of the austenitic stainless steel (12X18H12T), a metallographic analysis of the sigma phase percentage should be made. Following steel microstructure preparation, a series of microscopic digital images are used to measure this parameter. The digital images contain low amount of Gaussian noise and the sigma phase particles must be separated from all non-metal and other small-size or noise inclusions. Implementation of automated measurement leads to more accurate results and minimizes the subjective evaluation factors. A set of morphological features for each blob in a test group of blobs is analyzed using Kohonen self-organizing neural network after applying image filtering and blob detection algorithm. Self-organizing maps are used to filter the blobs. The achieved results are compared with those, obtained from the application of other metallographic methods for the same purpose.

ABSTRACT Typical application of machine vision systems in the discrete automated production is qu... more ABSTRACT Typical application of machine vision systems in the discrete automated production is quality control, measurement or classification of moving parts, placed on conveyor belts. Different technical issues (lighting problems, vibrations near camera or conveyor belt, etc.) can lead to noisy images and to wrong classifications or faulty measurements by the vision inspection system. The correlation between motion blur noise (added by technical malfunctions) and the correct measurement by the machine vision system is examined in this paper. First part of the study is to define the influence of motion blur to visual inspection of moving parts with linear velocity of up to 25 m/min. The analyzed vision inspections are size measurement, classification, OCR and code readings. A second study is performed to derive and to propose additional image filtration or vision inspection steps to minimize the wrong measurements according to the inspection type. Of great importance is the added additional amount of processing time. This requires accurate benchmarking of the proposed algorithms within similar laboratory conditions.

Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium .., 2011

A model for image and data pre-processing and communication between a dedicated PC and a PLC with... more A model for image and data pre-processing and communication between a dedicated PC and a PLC with Neural Network (NN) application is proposed in this paper. The proposed model defines guidelines for creating a multithreaded application for receiving real-time data from several digital cameras, parallel image pre-processing based on predefined user algorithms, calculation of input data vector for NN and

ABSTRACT The neural networks find many applications today in different kinds of real-time working... more ABSTRACT The neural networks find many applications today in different kinds of real-time working systems. To obtain short execution times and high recognition accuracy in real-time decision-making systems becomes a question of first importance. Therefore, the requirements to the recognition stage in such systems in reference to reduce the reaction time grow up. In the proposed research a new method for optimization of a MLP network structure for a real-time programmable logic controllers (PLC) application is presented. The optimization is accomplished in two steps. First the DCT coefficients are calculated over radial profiles of the objects which form a vector in the frequency parametrical space. This vector describes the corresponding 2D object and is applied as Initial Input Set to the MLP neural network structure. The size of each input for MLP vector is reduced applying modified coefficient of variations (MCV) to evaluate the outlier values. Second the reduced input set is divided and grouped into a number of small MLPs based on analysis of the degree of correlation between the inputs. The trained MLPs are downloaded in a Siemens PLC S7-300 for on-line real-time work in a parallel recognition mode. The proposed optimization is tested for four different 2D objects captured by a CCD matrix camera. The achieved results are represented and analyzed.

ABSTRACT The subjective evaluation of marbles based on their visual appearance could be replaced ... more ABSTRACT The subjective evaluation of marbles based on their visual appearance could be replaced by an automated texture classification system, intending to achieve high classification accuracy and production effectiveness. The existing marble classification methods from a computational point of view are either too complex or very expensive. Nowadays some inspection systems in marble industry that automates the quality-control tasks and shade classification are too expensive and are compatible only with specific technological equipment. In this paper a new approach for classification of marble tiles with similar shades is proposed. It is based on simple image preprocessing, on training a MLP neural network (MLP NN) with marble histograms and implementation of the algorithm in a Programmable Logic Controller (PLC) for real-time execution. A method for training the MLP NN aiming optimization of MLP parameters and topology is proposed. The designed automated system uses only standard PLC modules and communication interfaces. The experimental test results when recognizing marble textures with added motion blur are represented and discussed. The performance of the modeling technique is assessed with different training and test sets. The classification accuracy results are compared to other results obtained by similar approaches.

Tracking temperature changes in certain geographic regions is a current task in modern research o... more Tracking temperature changes in certain geographic regions is a current task in modern research on Earth's climate changes. One of the global problems in solving this task is related to the large volume of measured data and the search for appropriate methods for effective determination of changes. The purpose of this research is to track climate temperature changes using a machine learning-based automated change detection method. The presented method includes training of a two-level structure of neural networks, with measured temperatures for a ten-year period of time for a certain geographical region. In the testing phase, the neural structure classifies measured temperatures for two three-year periods, before and after the ten-year time period, respectively, for the same geographic region. An algorithm was developed to visualize the studied regions by creating a map with their geographic coordinates. The classification results in the neural structure outputs are presented and ...

Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium .., 2022

2022 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)